The Volcker Rule mandates that banking entities cease proprietary trading, subject to certain exceptions for “permitted activities” including market making and risk-mitigating hedging.

The current proposed implementation of the rule includes recommendations for a framework of 17 quantitative metrics to be calculated and analyzed daily, and reported to regulators monthly.

The 17 quantitative metrics are grouped into 5 metrics groups (as listed to the left)

Each metrics group variously seeks to establish that a bank’s risk taking appetite, revenue profile, trading inventory and origination are all consistent with that of a market maker providing liquidity and hedging any residual risks incurred in the provision of this service.

Risk Management: the 4 metrics in this group try to establish that the bank’s trading units retain risk that is not in excess of the size and type required to provide intermediation/market making services to customers.

Sources of Revenue: the 5 metrics in this group try to establish that the bank’s trading units’ revenues are earned primarily from customer revenues (fees, commissions and bid-offer spreads) and not from price movements of retained principal positions.

The paper examines the practice of PLA production, analysis and reporting within banks. Given the recent regulatory focus on PLA and banks’ capacity to produce it, the report also examines areas of potential interest i.e. policy, governance, process capacity and metrics that can be used to benchmark the bank’s capacity to produce, analyze, monitor and report PLA.

“Pray don’t talk to me about the weather, Mr. Worthing. Whenever people talk to me about the weather, I always feel quite certain that they mean something else. And that makes me so nervous.” – Oscar Wilde, The Importance of Being Earnest, Act 1

We will talk about weathermen and the predictions they make. And we will mean something entirely different. By weathermen, we will mean the models in a bank and the predictions they make or the hypotheses they form. And for the realism of Dr. Seuss’ drops dropping, we will substitute the realism of P&L.. More specifically, we will talk about P&L attribution (PLA) and the role it plays in helping us use the realism of P&L to test the hypotheses posed by our various risk models – which actually is its primary purpose in life.

We will focus specifically on 3 hypotheses formulated by a bank’s risk models, its VAR model and its CVA/EPE model respectively. Namely, for a given bank:

I. Change in the mark-to-market value of its positions are materially determined by changes to a specified set of variables and parameters (i.e. risk factors) and the expected change is quantified by the sensitivities obtained to these risk factors from its models;

II. There is a specified % probability that the value of its positions will lose more than its VAR number over any given interval equal to the VAR holding period;

III. The cost of insuring its aggregate positions against the risk of counterparty Z defaulting is not expected to exceed the cumulative sum of the CVA fees charged to its trading desks for originating exposure to counterparty Z.